library(here)
library(tidyverse)
theme_set(theme_light())
library(scuttle)
library(kableExtra)
library(DDCompanion)
Setup
## Directory setup
here_root <- "benchmarks/lupus-n_patients"
here::i_am(file.path(here_root, "analysis/lupus-n_patients-mock-results.Rmd"))
#> here() starts at /kyukon/data/gent/vo/000/gvo00063/Jeroen/DD_project_v2
res_dir <- here::here(here_root, "results")
fig_dir <- here::here(here_root, "figures")
- Using
n_patients: 10 20 30
- Using
methods: edgeR_NB, edgeR_QP, edgeR_NB_optim, edgeR_QP_optim, bGLM, qbGLM, qbGLM_offset, qbGLM_offset_squeeze
- Using
celltype: B_mem
Load results
# TODO: also add all-patients results for comparison?
res_files <- map(n_patients, ~ get_mock_res_files(
dataset = "lupus-n_patients",
methods = methods, celltype = celltype, datatype = "pb",
n_patients = .x
)) %>%
set_names(paste0("n_patients_", n_patients))
#res_list <- map_depth(res_files, 2, readRDS)
res_list <- lapply(res_files, function(element){
lapply(element, readRDS)
})
str(res_list, max.level = 3)
#> List of 3
#> $ n_patients_10:List of 8
#> ..$ edgeR_NB :List of 5
#> .. ..$ replicate_1:List of 2
#> .. ..$ replicate_2:List of 2
#> .. ..$ replicate_3:List of 2
#> .. ..$ replicate_4:List of 2
#> .. ..$ replicate_5:List of 2
#> ..$ edgeR_QP :List of 5
#> .. ..$ replicate_1:List of 2
#> .. ..$ replicate_2:List of 2
#> .. ..$ replicate_3:List of 2
#> .. ..$ replicate_4:List of 2
#> .. ..$ replicate_5:List of 2
#> ..$ edgeR_NB_optim :List of 5
#> .. ..$ replicate_1:List of 2
#> .. ..$ replicate_2:List of 2
#> .. ..$ replicate_3:List of 2
#> .. ..$ replicate_4:List of 2
#> .. ..$ replicate_5:List of 2
#> ..$ edgeR_QP_optim :List of 5
#> .. ..$ replicate_1:List of 2
#> .. ..$ replicate_2:List of 2
#> .. ..$ replicate_3:List of 2
#> .. ..$ replicate_4:List of 2
#> .. ..$ replicate_5:List of 2
#> ..$ bGLM :List of 5
#> .. ..$ replicate_1:List of 2
#> .. ..$ replicate_2:List of 2
#> .. ..$ replicate_3:List of 2
#> .. ..$ replicate_4:List of 2
#> .. ..$ replicate_5:List of 2
#> ..$ qbGLM :List of 5
#> .. ..$ replicate_1:List of 2
#> .. ..$ replicate_2:List of 2
#> .. ..$ replicate_3:List of 2
#> .. ..$ replicate_4:List of 2
#> .. ..$ replicate_5:List of 2
#> ..$ qbGLM_offset :List of 5
#> .. ..$ replicate_1:List of 2
#> .. ..$ replicate_2:List of 2
#> .. ..$ replicate_3:List of 2
#> .. ..$ replicate_4:List of 2
#> .. ..$ replicate_5:List of 2
#> ..$ qbGLM_offset_squeeze:List of 5
#> .. ..$ replicate_1:List of 2
#> .. ..$ replicate_2:List of 2
#> .. ..$ replicate_3:List of 2
#> .. ..$ replicate_4:List of 2
#> .. ..$ replicate_5:List of 2
#> $ n_patients_20:List of 8
#> ..$ edgeR_NB :List of 5
#> .. ..$ replicate_1:List of 2
#> .. ..$ replicate_2:List of 2
#> .. ..$ replicate_3:List of 2
#> .. ..$ replicate_4:List of 2
#> .. ..$ replicate_5:List of 2
#> ..$ edgeR_QP :List of 5
#> .. ..$ replicate_1:List of 2
#> .. ..$ replicate_2:List of 2
#> .. ..$ replicate_3:List of 2
#> .. ..$ replicate_4:List of 2
#> .. ..$ replicate_5:List of 2
#> ..$ edgeR_NB_optim :List of 5
#> .. ..$ replicate_1:List of 2
#> .. ..$ replicate_2:List of 2
#> .. ..$ replicate_3:List of 2
#> .. ..$ replicate_4:List of 2
#> .. ..$ replicate_5:List of 2
#> ..$ edgeR_QP_optim :List of 5
#> .. ..$ replicate_1:List of 2
#> .. ..$ replicate_2:List of 2
#> .. ..$ replicate_3:List of 2
#> .. ..$ replicate_4:List of 2
#> .. ..$ replicate_5:List of 2
#> ..$ bGLM :List of 5
#> .. ..$ replicate_1:List of 2
#> .. ..$ replicate_2:List of 2
#> .. ..$ replicate_3:List of 2
#> .. ..$ replicate_4:List of 2
#> .. ..$ replicate_5:List of 2
#> ..$ qbGLM :List of 5
#> .. ..$ replicate_1:List of 2
#> .. ..$ replicate_2:List of 2
#> .. ..$ replicate_3:List of 2
#> .. ..$ replicate_4:List of 2
#> .. ..$ replicate_5:List of 2
#> ..$ qbGLM_offset :List of 5
#> .. ..$ replicate_1:List of 2
#> .. ..$ replicate_2:List of 2
#> .. ..$ replicate_3:List of 2
#> .. ..$ replicate_4:List of 2
#> .. ..$ replicate_5:List of 2
#> ..$ qbGLM_offset_squeeze:List of 5
#> .. ..$ replicate_1:List of 2
#> .. ..$ replicate_2:List of 2
#> .. ..$ replicate_3:List of 2
#> .. ..$ replicate_4:List of 2
#> .. ..$ replicate_5:List of 2
#> $ n_patients_30:List of 8
#> ..$ edgeR_NB :List of 5
#> .. ..$ replicate_1:List of 2
#> .. ..$ replicate_2:List of 2
#> .. ..$ replicate_3:List of 2
#> .. ..$ replicate_4:List of 2
#> .. ..$ replicate_5:List of 2
#> ..$ edgeR_QP :List of 5
#> .. ..$ replicate_1:List of 2
#> .. ..$ replicate_2:List of 2
#> .. ..$ replicate_3:List of 2
#> .. ..$ replicate_4:List of 2
#> .. ..$ replicate_5:List of 2
#> ..$ edgeR_NB_optim :List of 5
#> .. ..$ replicate_1:List of 2
#> .. ..$ replicate_2:List of 2
#> .. ..$ replicate_3:List of 2
#> .. ..$ replicate_4:List of 2
#> .. ..$ replicate_5:List of 2
#> ..$ edgeR_QP_optim :List of 5
#> .. ..$ replicate_1:List of 2
#> .. ..$ replicate_2:List of 2
#> .. ..$ replicate_3:List of 2
#> .. ..$ replicate_4:List of 2
#> .. ..$ replicate_5:List of 2
#> ..$ bGLM :List of 5
#> .. ..$ replicate_1:List of 2
#> .. ..$ replicate_2:List of 2
#> .. ..$ replicate_3:List of 2
#> .. ..$ replicate_4:List of 2
#> .. ..$ replicate_5:List of 2
#> ..$ qbGLM :List of 5
#> .. ..$ replicate_1:List of 2
#> .. ..$ replicate_2:List of 2
#> .. ..$ replicate_3:List of 2
#> .. ..$ replicate_4:List of 2
#> .. ..$ replicate_5:List of 2
#> ..$ qbGLM_offset :List of 5
#> .. ..$ replicate_1:List of 2
#> .. ..$ replicate_2:List of 2
#> .. ..$ replicate_3:List of 2
#> .. ..$ replicate_4:List of 2
#> .. ..$ replicate_5:List of 2
#> ..$ qbGLM_offset_squeeze:List of 5
#> .. ..$ replicate_1:List of 2
#> .. ..$ replicate_2:List of 2
#> .. ..$ replicate_3:List of 2
#> .. ..$ replicate_4:List of 2
#> .. ..$ replicate_5:List of 2
Load SCE objects
data_files <- map(n_patients, ~ get_SCE_files(
dataset = "lupus-n_patients", which = "mock_replicates",
celltype = celltype, n_patients = .x
)) %>%
set_names(paste0("n_patients_", n_patients))
sce_objects <- map(data_files, readRDS)
str(sce_objects, max.level = 3)
#> List of 3
#> $ n_patients_10:List of 5
#> ..$ replicate_1:Formal class 'SingleCellExperiment' [package "SingleCellExperiment"] with 9 slots
#> ..$ replicate_2:Formal class 'SingleCellExperiment' [package "SingleCellExperiment"] with 9 slots
#> ..$ replicate_3:Formal class 'SingleCellExperiment' [package "SingleCellExperiment"] with 9 slots
#> ..$ replicate_4:Formal class 'SingleCellExperiment' [package "SingleCellExperiment"] with 9 slots
#> ..$ replicate_5:Formal class 'SingleCellExperiment' [package "SingleCellExperiment"] with 9 slots
#> $ n_patients_20:List of 5
#> ..$ replicate_1:Formal class 'SingleCellExperiment' [package "SingleCellExperiment"] with 9 slots
#> ..$ replicate_2:Formal class 'SingleCellExperiment' [package "SingleCellExperiment"] with 9 slots
#> ..$ replicate_3:Formal class 'SingleCellExperiment' [package "SingleCellExperiment"] with 9 slots
#> ..$ replicate_4:Formal class 'SingleCellExperiment' [package "SingleCellExperiment"] with 9 slots
#> ..$ replicate_5:Formal class 'SingleCellExperiment' [package "SingleCellExperiment"] with 9 slots
#> $ n_patients_30:List of 5
#> ..$ replicate_1:Formal class 'SingleCellExperiment' [package "SingleCellExperiment"] with 9 slots
#> ..$ replicate_2:Formal class 'SingleCellExperiment' [package "SingleCellExperiment"] with 9 slots
#> ..$ replicate_3:Formal class 'SingleCellExperiment' [package "SingleCellExperiment"] with 9 slots
#> ..$ replicate_4:Formal class 'SingleCellExperiment' [package "SingleCellExperiment"] with 9 slots
#> ..$ replicate_5:Formal class 'SingleCellExperiment' [package "SingleCellExperiment"] with 9 slots
Data overview
- The results were generated from 5 mock replicates
- Each replicate was generated by randomly splitting the subjects in two mock groups
- No sub-sampling of cells per patient was performed for this data
- The number of patients was randomly sub-sampled stratified by mock group
Note that for n_patients 10 and 20, the selected patients vary randomly between the replicates, which is why the nrows and ncols are not identical. For the n_patients 30, however, no random sub-sampling was done because I could just use all patients from 2 out of the original 3 batches from the Control samples. That’s why the nrows and ncols are identical for those subsets.
The mock group assignment of each subject also varies across replicates.
map_dfr(sce_objects,
~ map_dfr(.x, function(x) c(nrows = nrow(x), ncols = ncol(x)),
.id = "replicate"
),
.id = "n_patients"
)
Subjects are divided across mock groups as follows:
n_patients = 10
replicate_1
|
|
A
|
B
|
|
IGTB1762_IGTB1762
|
1
|
0
|
|
IGTB1793_IGTB1793
|
0
|
1
|
|
IGTB1815_IGTB1815
|
0
|
1
|
|
IGTB1828_IGTB1828
|
1
|
0
|
|
IGTB1871_IGTB1871
|
0
|
1
|
|
IGTB1895_IGTB1895
|
1
|
0
|
|
IGTB1982_IGTB1982
|
0
|
1
|
|
IGTB1996_IGTB1996
|
1
|
0
|
|
IGTB2007_IGTB2007
|
1
|
0
|
|
IGTB2065_IGTB2065
|
0
|
1
|
replicate_2
|
|
A
|
B
|
|
IGTB1762_IGTB1762
|
0
|
1
|
|
IGTB1789_IGTB1789
|
0
|
1
|
|
IGTB1828_IGTB1828
|
1
|
0
|
|
IGTB1840_IGTB1840
|
0
|
1
|
|
IGTB1871_IGTB1871
|
1
|
0
|
|
IGTB1895_IGTB1895
|
0
|
1
|
|
IGTB1901_IGTB1901
|
1
|
0
|
|
IGTB1952_IGTB1952
|
1
|
0
|
|
IGTB1996_IGTB1996
|
0
|
1
|
|
IGTB2007_IGTB2007
|
1
|
0
|
replicate_3
|
|
A
|
B
|
|
IGTB1762_IGTB1762
|
0
|
1
|
|
IGTB1789_IGTB1789
|
1
|
0
|
|
IGTB1793_IGTB1793
|
1
|
0
|
|
IGTB1815_IGTB1815
|
0
|
1
|
|
IGTB1895_IGTB1895
|
1
|
0
|
|
IGTB1952_IGTB1952
|
0
|
1
|
|
IGTB1966_IGTB1966
|
1
|
0
|
|
IGTB1996_IGTB1996
|
0
|
1
|
|
IGTB2007_IGTB2007
|
1
|
0
|
|
IGTB2065_IGTB2065
|
0
|
1
|
replicate_4
|
|
A
|
B
|
|
IGTB1762_IGTB1762
|
0
|
1
|
|
IGTB1789_IGTB1789
|
1
|
0
|
|
IGTB1815_IGTB1815
|
1
|
0
|
|
IGTB1828_IGTB1828
|
0
|
1
|
|
IGTB1895_IGTB1895
|
0
|
1
|
|
IGTB1901_IGTB1901
|
1
|
0
|
|
IGTB1952_IGTB1952
|
0
|
1
|
|
IGTB1982_IGTB1982
|
0
|
1
|
|
IGTB1996_IGTB1996
|
1
|
0
|
|
IGTB2065_IGTB2065
|
1
|
0
|
replicate_5
|
|
A
|
B
|
|
IGTB1762_IGTB1762
|
1
|
0
|
|
IGTB1789_IGTB1789
|
1
|
0
|
|
IGTB1793_IGTB1793
|
0
|
1
|
|
IGTB1815_IGTB1815
|
0
|
1
|
|
IGTB1828_IGTB1828
|
0
|
1
|
|
IGTB1840_IGTB1840
|
0
|
1
|
|
IGTB1901_IGTB1901
|
1
|
0
|
|
IGTB1996_IGTB1996
|
0
|
1
|
|
IGTB2007_IGTB2007
|
1
|
0
|
|
IGTB2065_IGTB2065
|
1
|
0
|
n_patients = 20
replicate_1
|
|
A
|
B
|
|
IGTB141_IGTB141
|
1
|
0
|
|
IGTB143_IGTB143
|
1
|
0
|
|
IGTB195_IGTB195
|
0
|
1
|
|
IGTB469_IGTB469
|
1
|
0
|
|
IGTB498_IGTB498
|
1
|
0
|
|
IGTB508_IGTB508
|
1
|
0
|
|
IGTB514_IGTB514
|
1
|
0
|
|
IGTB826_IGTB826
|
0
|
1
|
|
IGTB1506_IGTB1506
|
0
|
1
|
|
IGTB1762_IGTB1762
|
1
|
0
|
|
IGTB1789_IGTB1789
|
0
|
1
|
|
IGTB1793_IGTB1793
|
0
|
1
|
|
IGTB1840_IGTB1840
|
0
|
1
|
|
IGTB1871_IGTB1871
|
0
|
1
|
|
IGTB1895_IGTB1895
|
1
|
0
|
|
IGTB1952_IGTB1952
|
1
|
0
|
|
IGTB1966_IGTB1966
|
0
|
1
|
|
IGTB1982_IGTB1982
|
0
|
1
|
|
IGTB1996_IGTB1996
|
1
|
0
|
|
IGTB2065_IGTB2065
|
0
|
1
|
replicate_2
|
|
A
|
B
|
|
IGTB141_IGTB141
|
1
|
0
|
|
IGTB143_IGTB143
|
0
|
1
|
|
IGTB195_IGTB195
|
1
|
0
|
|
IGTB469_IGTB469
|
1
|
0
|
|
IGTB508_IGTB508
|
0
|
1
|
|
IGTB514_IGTB514
|
0
|
1
|
|
IGTB670_IGTB670
|
0
|
1
|
|
IGTB826_IGTB826
|
1
|
0
|
|
IGTB1506_IGTB1506
|
0
|
1
|
|
IGTB1575_IGTB1575
|
1
|
0
|
|
IGTB1762_IGTB1762
|
0
|
1
|
|
IGTB1789_IGTB1789
|
0
|
1
|
|
IGTB1793_IGTB1793
|
0
|
1
|
|
IGTB1840_IGTB1840
|
0
|
1
|
|
IGTB1871_IGTB1871
|
1
|
0
|
|
IGTB1901_IGTB1901
|
1
|
0
|
|
IGTB1966_IGTB1966
|
0
|
1
|
|
IGTB1982_IGTB1982
|
1
|
0
|
|
IGTB2007_IGTB2007
|
1
|
0
|
|
IGTB2065_IGTB2065
|
1
|
0
|
replicate_3
|
|
A
|
B
|
|
IGTB143_IGTB143
|
0
|
1
|
|
IGTB195_IGTB195
|
0
|
1
|
|
IGTB498_IGTB498
|
0
|
1
|
|
IGTB508_IGTB508
|
0
|
1
|
|
IGTB514_IGTB514
|
0
|
1
|
|
IGTB645_IGTB645
|
0
|
1
|
|
IGTB670_IGTB670
|
1
|
0
|
|
IGTB826_IGTB826
|
1
|
0
|
|
IGTB1372_IGTB1372
|
1
|
0
|
|
IGTB1539_IGTB1539
|
1
|
0
|
|
IGTB1575_IGTB1575
|
1
|
0
|
|
IGTB1789_IGTB1789
|
1
|
0
|
|
IGTB1815_IGTB1815
|
0
|
1
|
|
IGTB1828_IGTB1828
|
1
|
0
|
|
IGTB1901_IGTB1901
|
1
|
0
|
|
IGTB1906_IGTB1906
|
1
|
0
|
|
IGTB1952_IGTB1952
|
0
|
1
|
|
IGTB1982_IGTB1982
|
0
|
1
|
|
IGTB2007_IGTB2007
|
1
|
0
|
|
IGTB2065_IGTB2065
|
0
|
1
|
replicate_4
|
|
A
|
B
|
|
IGTB143_IGTB143
|
1
|
0
|
|
IGTB469_IGTB469
|
0
|
1
|
|
IGTB498_IGTB498
|
0
|
1
|
|
IGTB670_IGTB670
|
1
|
0
|
|
IGTB1372_IGTB1372
|
0
|
1
|
|
IGTB1506_IGTB1506
|
0
|
1
|
|
IGTB1539_IGTB1539
|
1
|
0
|
|
IGTB1575_IGTB1575
|
1
|
0
|
|
IGTB1762_IGTB1762
|
0
|
1
|
|
IGTB1789_IGTB1789
|
1
|
0
|
|
IGTB1840_IGTB1840
|
0
|
1
|
|
IGTB1871_IGTB1871
|
0
|
1
|
|
IGTB1895_IGTB1895
|
0
|
1
|
|
IGTB1901_IGTB1901
|
1
|
0
|
|
IGTB1906_IGTB1906
|
1
|
0
|
|
IGTB1952_IGTB1952
|
0
|
1
|
|
IGTB1966_IGTB1966
|
1
|
0
|
|
IGTB1982_IGTB1982
|
0
|
1
|
|
IGTB2007_IGTB2007
|
1
|
0
|
|
IGTB2065_IGTB2065
|
1
|
0
|
replicate_5
|
|
A
|
B
|
|
IGTB141_IGTB141
|
0
|
1
|
|
IGTB143_IGTB143
|
0
|
1
|
|
IGTB498_IGTB498
|
1
|
0
|
|
IGTB508_IGTB508
|
0
|
1
|
|
IGTB514_IGTB514
|
1
|
0
|
|
IGTB645_IGTB645
|
0
|
1
|
|
IGTB1506_IGTB1506
|
0
|
1
|
|
IGTB1539_IGTB1539
|
1
|
0
|
|
IGTB1575_IGTB1575
|
1
|
0
|
|
IGTB1762_IGTB1762
|
1
|
0
|
|
IGTB1789_IGTB1789
|
1
|
0
|
|
IGTB1815_IGTB1815
|
0
|
1
|
|
IGTB1840_IGTB1840
|
0
|
1
|
|
IGTB1895_IGTB1895
|
1
|
0
|
|
IGTB1901_IGTB1901
|
1
|
0
|
|
IGTB1906_IGTB1906
|
0
|
1
|
|
IGTB1966_IGTB1966
|
1
|
0
|
|
IGTB1982_IGTB1982
|
0
|
1
|
|
IGTB1996_IGTB1996
|
0
|
1
|
|
IGTB2065_IGTB2065
|
1
|
0
|
n_patients = 30
replicate_1
|
|
A
|
B
|
|
IGTB141_IGTB141
|
1
|
0
|
|
IGTB143_IGTB143
|
1
|
0
|
|
IGTB195_IGTB195
|
0
|
1
|
|
IGTB469_IGTB469
|
1
|
0
|
|
IGTB498_IGTB498
|
1
|
0
|
|
IGTB508_IGTB508
|
1
|
0
|
|
IGTB514_IGTB514
|
1
|
0
|
|
IGTB645_IGTB645
|
1
|
0
|
|
IGTB670_IGTB670
|
0
|
1
|
|
IGTB826_IGTB826
|
0
|
1
|
|
IGTB1372_IGTB1372
|
0
|
1
|
|
IGTB1506_IGTB1506
|
0
|
1
|
|
IGTB1539_IGTB1539
|
1
|
0
|
|
IGTB1575_IGTB1575
|
0
|
1
|
|
IGTB1762_IGTB1762
|
1
|
0
|
|
IGTB1789_IGTB1789
|
0
|
1
|
|
IGTB1793_IGTB1793
|
0
|
1
|
|
IGTB1815_IGTB1815
|
0
|
1
|
|
IGTB1828_IGTB1828
|
1
|
0
|
|
IGTB1840_IGTB1840
|
0
|
1
|
|
IGTB1871_IGTB1871
|
0
|
1
|
|
IGTB1895_IGTB1895
|
1
|
0
|
|
IGTB1901_IGTB1901
|
1
|
0
|
|
IGTB1906_IGTB1906
|
0
|
1
|
|
IGTB1952_IGTB1952
|
1
|
0
|
|
IGTB1966_IGTB1966
|
0
|
1
|
|
IGTB1982_IGTB1982
|
0
|
1
|
|
IGTB1996_IGTB1996
|
1
|
0
|
|
IGTB2007_IGTB2007
|
1
|
0
|
|
IGTB2065_IGTB2065
|
0
|
1
|
replicate_2
|
|
A
|
B
|
|
IGTB141_IGTB141
|
1
|
0
|
|
IGTB143_IGTB143
|
0
|
1
|
|
IGTB195_IGTB195
|
1
|
0
|
|
IGTB469_IGTB469
|
1
|
0
|
|
IGTB498_IGTB498
|
0
|
1
|
|
IGTB508_IGTB508
|
0
|
1
|
|
IGTB514_IGTB514
|
0
|
1
|
|
IGTB645_IGTB645
|
0
|
1
|
|
IGTB670_IGTB670
|
0
|
1
|
|
IGTB826_IGTB826
|
1
|
0
|
|
IGTB1372_IGTB1372
|
0
|
1
|
|
IGTB1506_IGTB1506
|
0
|
1
|
|
IGTB1539_IGTB1539
|
1
|
0
|
|
IGTB1575_IGTB1575
|
1
|
0
|
|
IGTB1762_IGTB1762
|
0
|
1
|
|
IGTB1789_IGTB1789
|
0
|
1
|
|
IGTB1793_IGTB1793
|
0
|
1
|
|
IGTB1815_IGTB1815
|
1
|
0
|
|
IGTB1828_IGTB1828
|
1
|
0
|
|
IGTB1840_IGTB1840
|
0
|
1
|
|
IGTB1871_IGTB1871
|
1
|
0
|
|
IGTB1895_IGTB1895
|
0
|
1
|
|
IGTB1901_IGTB1901
|
1
|
0
|
|
IGTB1906_IGTB1906
|
1
|
0
|
|
IGTB1952_IGTB1952
|
1
|
0
|
|
IGTB1966_IGTB1966
|
0
|
1
|
|
IGTB1982_IGTB1982
|
1
|
0
|
|
IGTB1996_IGTB1996
|
0
|
1
|
|
IGTB2007_IGTB2007
|
1
|
0
|
|
IGTB2065_IGTB2065
|
1
|
0
|
replicate_3
|
|
A
|
B
|
|
IGTB141_IGTB141
|
0
|
1
|
|
IGTB143_IGTB143
|
0
|
1
|
|
IGTB195_IGTB195
|
0
|
1
|
|
IGTB469_IGTB469
|
1
|
0
|
|
IGTB498_IGTB498
|
0
|
1
|
|
IGTB508_IGTB508
|
0
|
1
|
|
IGTB514_IGTB514
|
0
|
1
|
|
IGTB645_IGTB645
|
0
|
1
|
|
IGTB670_IGTB670
|
1
|
0
|
|
IGTB826_IGTB826
|
1
|
0
|
|
IGTB1372_IGTB1372
|
1
|
0
|
|
IGTB1506_IGTB1506
|
1
|
0
|
|
IGTB1539_IGTB1539
|
1
|
0
|
|
IGTB1575_IGTB1575
|
1
|
0
|
|
IGTB1762_IGTB1762
|
0
|
1
|
|
IGTB1789_IGTB1789
|
1
|
0
|
|
IGTB1793_IGTB1793
|
1
|
0
|
|
IGTB1815_IGTB1815
|
0
|
1
|
|
IGTB1828_IGTB1828
|
1
|
0
|
|
IGTB1840_IGTB1840
|
1
|
0
|
|
IGTB1871_IGTB1871
|
0
|
1
|
|
IGTB1895_IGTB1895
|
1
|
0
|
|
IGTB1901_IGTB1901
|
1
|
0
|
|
IGTB1906_IGTB1906
|
1
|
0
|
|
IGTB1952_IGTB1952
|
0
|
1
|
|
IGTB1966_IGTB1966
|
1
|
0
|
|
IGTB1982_IGTB1982
|
0
|
1
|
|
IGTB1996_IGTB1996
|
0
|
1
|
|
IGTB2007_IGTB2007
|
1
|
0
|
|
IGTB2065_IGTB2065
|
0
|
1
|
replicate_4
|
|
A
|
B
|
|
IGTB141_IGTB141
|
0
|
1
|
|
IGTB143_IGTB143
|
1
|
0
|
|
IGTB195_IGTB195
|
1
|
0
|
|
IGTB469_IGTB469
|
0
|
1
|
|
IGTB498_IGTB498
|
0
|
1
|
|
IGTB508_IGTB508
|
0
|
1
|
|
IGTB514_IGTB514
|
1
|
0
|
|
IGTB645_IGTB645
|
0
|
1
|
|
IGTB670_IGTB670
|
1
|
0
|
|
IGTB826_IGTB826
|
0
|
1
|
|
IGTB1372_IGTB1372
|
0
|
1
|
|
IGTB1506_IGTB1506
|
0
|
1
|
|
IGTB1539_IGTB1539
|
1
|
0
|
|
IGTB1575_IGTB1575
|
1
|
0
|
|
IGTB1762_IGTB1762
|
0
|
1
|
|
IGTB1789_IGTB1789
|
1
|
0
|
|
IGTB1793_IGTB1793
|
0
|
1
|
|
IGTB1815_IGTB1815
|
1
|
0
|
|
IGTB1828_IGTB1828
|
0
|
1
|
|
IGTB1840_IGTB1840
|
0
|
1
|
|
IGTB1871_IGTB1871
|
0
|
1
|
|
IGTB1895_IGTB1895
|
0
|
1
|
|
IGTB1901_IGTB1901
|
1
|
0
|
|
IGTB1906_IGTB1906
|
1
|
0
|
|
IGTB1952_IGTB1952
|
0
|
1
|
|
IGTB1966_IGTB1966
|
1
|
0
|
|
IGTB1982_IGTB1982
|
0
|
1
|
|
IGTB1996_IGTB1996
|
1
|
0
|
|
IGTB2007_IGTB2007
|
1
|
0
|
|
IGTB2065_IGTB2065
|
1
|
0
|
replicate_5
|
|
A
|
B
|
|
IGTB141_IGTB141
|
0
|
1
|
|
IGTB143_IGTB143
|
0
|
1
|
|
IGTB195_IGTB195
|
0
|
1
|
|
IGTB469_IGTB469
|
1
|
0
|
|
IGTB498_IGTB498
|
1
|
0
|
|
IGTB508_IGTB508
|
0
|
1
|
|
IGTB514_IGTB514
|
1
|
0
|
|
IGTB645_IGTB645
|
0
|
1
|
|
IGTB670_IGTB670
|
1
|
0
|
|
IGTB826_IGTB826
|
1
|
0
|
|
IGTB1372_IGTB1372
|
1
|
0
|
|
IGTB1506_IGTB1506
|
0
|
1
|
|
IGTB1539_IGTB1539
|
1
|
0
|
|
IGTB1575_IGTB1575
|
1
|
0
|
|
IGTB1762_IGTB1762
|
1
|
0
|
|
IGTB1789_IGTB1789
|
1
|
0
|
|
IGTB1793_IGTB1793
|
0
|
1
|
|
IGTB1815_IGTB1815
|
0
|
1
|
|
IGTB1828_IGTB1828
|
0
|
1
|
|
IGTB1840_IGTB1840
|
0
|
1
|
|
IGTB1871_IGTB1871
|
0
|
1
|
|
IGTB1895_IGTB1895
|
1
|
0
|
|
IGTB1901_IGTB1901
|
1
|
0
|
|
IGTB1906_IGTB1906
|
0
|
1
|
|
IGTB1952_IGTB1952
|
0
|
1
|
|
IGTB1966_IGTB1966
|
1
|
0
|
|
IGTB1982_IGTB1982
|
0
|
1
|
|
IGTB1996_IGTB1996
|
0
|
1
|
|
IGTB2007_IGTB2007
|
1
|
0
|
|
IGTB2065_IGTB2065
|
1
|
0
|
Visualize results
Run times
ggplot(runtimes, aes(n_patients, time, col = method)) +
geom_jitter(width = 0.5, alpha = 0.6) +
geom_smooth(se = FALSE, method = "lm", formula = y ~ x) +
labs(x = "Number of patients", y = "Time (seconds)", color = NULL) +
scale_x_continuous(breaks = unique(runtimes$n_patients), minor_breaks = NULL)

Number of significant genes for different FDR levels
n_sign_summaries <- map(res_tables, function(tmp) {
tmp %>%
group_by(replicate, method) %>%
summarise(
sum(FDR < 0.01),
sum(FDR < 0.05),
sum(FDR < 0.1),
.groups = "drop"
)
})
n_patients_10
|
method
|
sum(FDR < 0.01)
|
sum(FDR < 0.05)
|
sum(FDR < 0.1)
|
|
Replicate: 1
|
|
bGLM
|
10
|
20
|
44
|
|
edgeR_NB
|
0
|
0
|
0
|
|
edgeR_NB_optim
|
0
|
0
|
0
|
|
edgeR_QP
|
0
|
0
|
0
|
|
edgeR_QP_optim
|
0
|
0
|
0
|
|
qbGLM
|
0
|
0
|
1
|
|
qbGLM_offset
|
1
|
1
|
5
|
|
qbGLM_offset_squeeze
|
0
|
0
|
0
|
|
Replicate: 2
|
|
bGLM
|
13
|
26
|
65
|
|
edgeR_NB
|
0
|
0
|
0
|
|
edgeR_NB_optim
|
0
|
0
|
0
|
|
edgeR_QP
|
0
|
0
|
0
|
|
edgeR_QP_optim
|
0
|
0
|
0
|
|
qbGLM
|
0
|
0
|
0
|
|
qbGLM_offset
|
0
|
0
|
0
|
|
qbGLM_offset_squeeze
|
0
|
0
|
0
|
|
Replicate: 3
|
|
bGLM
|
12
|
31
|
68
|
|
edgeR_NB
|
0
|
0
|
0
|
|
edgeR_NB_optim
|
0
|
0
|
1
|
|
edgeR_QP
|
0
|
0
|
0
|
|
edgeR_QP_optim
|
0
|
1
|
1
|
|
qbGLM
|
0
|
0
|
0
|
|
qbGLM_offset
|
0
|
0
|
0
|
|
qbGLM_offset_squeeze
|
0
|
0
|
0
|
|
Replicate: 4
|
|
bGLM
|
30
|
72
|
109
|
|
edgeR_NB
|
0
|
0
|
0
|
|
edgeR_NB_optim
|
0
|
0
|
0
|
|
edgeR_QP
|
0
|
0
|
0
|
|
edgeR_QP_optim
|
0
|
0
|
0
|
|
qbGLM
|
0
|
0
|
0
|
|
qbGLM_offset
|
0
|
0
|
0
|
|
qbGLM_offset_squeeze
|
0
|
0
|
0
|
|
Replicate: 5
|
|
bGLM
|
52
|
104
|
158
|
|
edgeR_NB
|
0
|
0
|
0
|
|
edgeR_NB_optim
|
0
|
0
|
0
|
|
edgeR_QP
|
0
|
0
|
0
|
|
edgeR_QP_optim
|
0
|
0
|
0
|
|
qbGLM
|
0
|
0
|
0
|
|
qbGLM_offset
|
0
|
0
|
0
|
|
qbGLM_offset_squeeze
|
0
|
0
|
0
|
n_patients_20
|
method
|
sum(FDR < 0.01)
|
sum(FDR < 0.05)
|
sum(FDR < 0.1)
|
|
Replicate: 1
|
|
bGLM
|
5
|
10
|
22
|
|
edgeR_NB
|
0
|
0
|
0
|
|
edgeR_NB_optim
|
0
|
0
|
0
|
|
edgeR_QP
|
0
|
0
|
0
|
|
edgeR_QP_optim
|
0
|
0
|
0
|
|
qbGLM
|
0
|
0
|
0
|
|
qbGLM_offset
|
0
|
0
|
0
|
|
qbGLM_offset_squeeze
|
0
|
0
|
0
|
|
Replicate: 2
|
|
bGLM
|
23
|
57
|
85
|
|
edgeR_NB
|
0
|
0
|
0
|
|
edgeR_NB_optim
|
0
|
0
|
0
|
|
edgeR_QP
|
0
|
0
|
0
|
|
edgeR_QP_optim
|
0
|
0
|
0
|
|
qbGLM
|
0
|
0
|
0
|
|
qbGLM_offset
|
0
|
0
|
0
|
|
qbGLM_offset_squeeze
|
0
|
0
|
0
|
|
Replicate: 3
|
|
bGLM
|
10
|
17
|
22
|
|
edgeR_NB
|
0
|
0
|
0
|
|
edgeR_NB_optim
|
0
|
0
|
0
|
|
edgeR_QP
|
0
|
0
|
0
|
|
edgeR_QP_optim
|
0
|
0
|
0
|
|
qbGLM
|
0
|
0
|
0
|
|
qbGLM_offset
|
0
|
0
|
0
|
|
qbGLM_offset_squeeze
|
0
|
0
|
0
|
|
Replicate: 4
|
|
bGLM
|
9
|
12
|
14
|
|
edgeR_NB
|
0
|
0
|
0
|
|
edgeR_NB_optim
|
0
|
0
|
0
|
|
edgeR_QP
|
0
|
0
|
0
|
|
edgeR_QP_optim
|
0
|
0
|
0
|
|
qbGLM
|
0
|
0
|
0
|
|
qbGLM_offset
|
0
|
0
|
0
|
|
qbGLM_offset_squeeze
|
0
|
0
|
0
|
|
Replicate: 5
|
|
bGLM
|
5
|
6
|
11
|
|
edgeR_NB
|
0
|
0
|
0
|
|
edgeR_NB_optim
|
0
|
0
|
0
|
|
edgeR_QP
|
0
|
0
|
0
|
|
edgeR_QP_optim
|
0
|
0
|
0
|
|
qbGLM
|
0
|
0
|
0
|
|
qbGLM_offset
|
0
|
0
|
0
|
|
qbGLM_offset_squeeze
|
0
|
0
|
0
|
n_patients_30
|
method
|
sum(FDR < 0.01)
|
sum(FDR < 0.05)
|
sum(FDR < 0.1)
|
|
Replicate: 1
|
|
bGLM
|
5
|
13
|
18
|
|
edgeR_NB
|
0
|
0
|
0
|
|
edgeR_NB_optim
|
0
|
0
|
0
|
|
edgeR_QP
|
0
|
0
|
0
|
|
edgeR_QP_optim
|
0
|
0
|
0
|
|
qbGLM
|
0
|
0
|
0
|
|
qbGLM_offset
|
0
|
0
|
0
|
|
qbGLM_offset_squeeze
|
0
|
0
|
0
|
|
Replicate: 2
|
|
bGLM
|
27
|
55
|
81
|
|
edgeR_NB
|
0
|
0
|
0
|
|
edgeR_NB_optim
|
0
|
0
|
0
|
|
edgeR_QP
|
0
|
0
|
0
|
|
edgeR_QP_optim
|
0
|
0
|
0
|
|
qbGLM
|
0
|
0
|
1
|
|
qbGLM_offset
|
0
|
0
|
0
|
|
qbGLM_offset_squeeze
|
0
|
0
|
0
|
|
Replicate: 3
|
|
bGLM
|
8
|
30
|
46
|
|
edgeR_NB
|
0
|
0
|
0
|
|
edgeR_NB_optim
|
0
|
0
|
0
|
|
edgeR_QP
|
0
|
0
|
0
|
|
edgeR_QP_optim
|
0
|
0
|
0
|
|
qbGLM
|
0
|
0
|
0
|
|
qbGLM_offset
|
0
|
0
|
0
|
|
qbGLM_offset_squeeze
|
0
|
0
|
0
|
|
Replicate: 4
|
|
bGLM
|
11
|
21
|
25
|
|
edgeR_NB
|
0
|
0
|
0
|
|
edgeR_NB_optim
|
0
|
0
|
0
|
|
edgeR_QP
|
0
|
0
|
0
|
|
edgeR_QP_optim
|
0
|
0
|
0
|
|
qbGLM
|
0
|
0
|
0
|
|
qbGLM_offset
|
0
|
0
|
0
|
|
qbGLM_offset_squeeze
|
0
|
0
|
0
|
|
Replicate: 5
|
|
bGLM
|
8
|
13
|
18
|
|
edgeR_NB
|
0
|
0
|
0
|
|
edgeR_NB_optim
|
0
|
0
|
0
|
|
edgeR_QP
|
0
|
0
|
0
|
|
edgeR_QP_optim
|
0
|
0
|
0
|
|
qbGLM
|
0
|
0
|
0
|
|
qbGLM_offset
|
0
|
0
|
0
|
|
qbGLM_offset_squeeze
|
0
|
0
|
0
|
P-value distributions
pval_figs <- map(res_tables, pval_hist)
n_patients_10

n_patients_20

n_patients_30

P-value distributions stratified by expression level
For each replicate, calculate average log-normalized counts, using scuttle::calculateAverage().
## Calculate average counts for each replicate
avg_counts <- map_depth(sce_objects, 2,
~ calculateAverage(counts(.x))
)
## Split res_tables per replicate
res_tables_split <- map(res_tables, ~ split(.x, .x$replicate))
## Add average counts to results tables
## Note that for the 'combined' celltypes, the average counts will be the same
## for each celltype contrast
res_tables_split <- map2(
res_tables_split, avg_counts,
~ map2(.x, .y, function(res, avg_cnt) {
res$avg_count <- avg_cnt[res$gene]
res
})
)
Make p-value histograms stratified by the average counts.
pval_hist_strat_plots <- map(res_tables_split,
~ imap(.x, function(y, replicate) {
replicate <- str_remove(replicate, "replicate_")
title <- paste("Replicate:", replicate)
pval_hist_strat(y, n_groups = 6) +
ggtitle(title)
})
)
Session info
Session info
#> [1] "2023-06-02 20:24:26 CEST"
#> ─ Session info ───────────────────────────────────────────────────────────────
#> setting value
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#> language (EN)
#> collate en_US.UTF-8
#> ctype en_US.UTF-8
#> tz Europe/Paris
#> date 2023-06-02
#> pandoc 2.13 @ /apps/gent/RHEL8/zen2-ib/software/Pandoc/2.13/bin/ (via rmarkdown)
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